Game report data prediction model based on LSTM-GRU
DOI:
https://doi.org/10.54097/hset.v63i.10813Keywords:
LSTM-GRU; Prediction Model; Machine learning; Game repot data.Abstract
With the increasing number of participants in wordles game, the number of reports changed, we firstly preprocessed the data by sliding window, and then analyzed the reasons for the change of the number of reports and predicted them by LSTM+GRU time series prediction model, and got that the change of the number of reports increased slowly in the early stage and decreased gradually in the later stage, then we predicted the change of the number of reports on March 1, and its change The interval is [22233, 22407], and it has a good fitting effect. Finally, through the comparison of different models, this model has the smallest error, which increases the credibility of the model. Finally, it is found that if this game does not innovate in time, the number of people will gradually decrease in the later.
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